Sharing sensor data to assist in maneuvering of an autonomous vehicle

ABSTRACT

An autonomous vehicle including a sensor system configured to detect an environment in a field of view of the autonomous vehicle and to output data of the detected environment. A computing system of the autonomous vehicle can detect that a portion of a roadway along a route of the autonomous vehicle in the field of view of the autonomous vehicle is obscured. Responsive to the detection of the obscured portion of the roadway, the computing system can transmit a request for a second autonomous vehicle to view the portion of the roadway. The computing system can receive fill data representative of an output of a second sensor system of the second autonomous vehicle detecting the portion of the roadway in a field of view of the second autonomous vehicle.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/695,149, filed on Nov. 25, 2019, and entitled “SHARING SENSOR DATA TOASSIST IN MANEUVERING OF AN AUTONOMOUS VEHICLE”, the entirety of whichis incorporated herein by reference.

BACKGROUND

An autonomous vehicle is a vehicle that is configured to navigate aboutroadways based upon sensor signals outputted by sensors of theautonomous vehicle; the autonomous vehicle typically navigates theroadways without input from a human. The autonomous vehicle isconfigured to identify and track objects (such as vehicles, pedestrians,bicyclists, static objects, and so forth) based upon the sensor signalsoutput by the sensors of the autonomous vehicle and perform drivingmaneuvers (such as accelerating, decelerating, turning, stopping, etc.)based upon the identified and tracked objects.

There are scenarios, however, where the autonomous vehicle has no orlimited visibility with respect to a spatial region (e.g., due toocclusion by object(s) in the environment, road geometries, inherentsensor limitations, etc.). A spatial region where the autonomous vehiclehas no or limited visibility is referred to herein as an obstructedregion. There is uncertainty associated with this obstructed region, asthe autonomous vehicle may be unable to ascertain whether there is anobject in the obstructed region, whether the object is moving in theobstructed region, and so forth.

In an example, the autonomous vehicle may be traveling along a lane of aroadway and may encounter a truck that is stopped in the lane of theroadway (e.g., the truck may be double-parked, the truck may bedisabled, etc.). As the autonomous vehicle moves closer to the back ofthe truck, a portion of a field of view of a sensor of the autonomousvehicle is obstructed by the truck, resulting in an obstructed region(from the perspective of the autonomous vehicle) that includes a portionof the lane of the roadway behind the truck and at least a portion of anadjacent lane of the roadway (where the adjacent lane includes oncomingtraffic). Accordingly, the autonomous vehicle may lack visibility intothe obstructed region and be unable to ascertain whether to attempt todrive around the truck, which can detrimentally impact operation of theautonomous vehicle (e.g., the autonomous vehicle may stop for anextended period of time until the truck moves, a human operator may takeover control of the autonomous vehicle, or the like).

SUMMARY

The following is a brief summary of subject matter that is described ingreater detail herein. This summary is not intended to be limiting as toscope of the claims.

Described herein are various technologies pertaining to an autonomousvehicle that is configured to identify an obstructed region based upon asensor signal output by a sensor of the autonomous vehicle and isfurther configured to request a second autonomous vehicle view theobstructed region. With more specificity, the autonomous vehicle candefine the obstructed region by identifying a spatial region in anenvironment where the autonomous vehicle lacks visibility. Further, theautonomous vehicle can transmit a request for a second autonomousvehicle to view at least a part of the obstructed region. Moreover, theautonomous vehicle can receive data representative of a sensor signaloutput by a sensor of the second autonomous vehicle detecting at least apart of the obscured region.

In an example, the autonomous vehicle is traveling in a first lane of atwo-lane roadway, where traffic in a second lane of the two-lane roadwayflows in a direction that is opposite the flow of traffic in the firstlane. The autonomous vehicle, when travelling along a route, comes upona truck that is stopped in the first lane of the roadway. The autonomousvehicle identifies the truck and further identifies an obstructed regionbased upon the truck being identified and a location of the truckrelative to the location of the autonomous vehicle. The obscured region(e.g., the spatial region where the autonomous vehicle is “blind” due tothe existence of the truck) encompasses a portion of the second lane. Toreach its intended destination, the autonomous vehicle may need tocontinue travelling along the roadway and thus may exit the first laneand enter the second lane (of oncoming traffic) in order to pass thetruck.

In connection with detecting this obscured region, the autonomousvehicle can transmit a request for a second autonomous vehicle to viewat least a portion of the obscured region to determine whether an object(or objects) is in the second lane in the obscured region before theautonomous vehicle enters the second lane in order to pass the truck.For instance, the second autonomous vehicle can be traveling along aroute in the second lane and can identify whether a vehicle is travelingalong a portion of the second lane in the obstructed region (or someother object is in the second lane in the obstructed region). Theautonomous vehicle can then use this information to determine whether tomaneuver around the truck by entering the second lane.

The above-described technologies present various advantages overconventional approaches to autonomous vehicle operation. First, unlikethe conventional approach of requiring a human to take control of theautonomous vehicle to determine whether the obstructed region is free ofobjects (e.g., vehicles, pedestrians, bicyclists, static objects, etc.),the above-described technologies provide a system for a secondautonomous vehicle to provide data to fill in the obstructed region.Moreover, instead of relying on the autonomous vehicle to carry a devicefor detecting this obstructed region (e.g., a drone), theabove-described technologies leverage one or more autonomous vehiclesalready traveling in the region to providing fill data for theautonomous vehicle.

The above summary presents a simplified summary in order to provide abasic understanding of some aspects of the systems and/or methodsdiscussed herein. This summary is not an extensive overview of thesystems and/or methods discussed herein. It is not intended to identifykey/critical elements or to delineate the scope of such systems and/ormethods. Its sole purpose is to present some concepts in a simplifiedform as a prelude to the more detailed description that is presentedlater.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic that illustrates an autonomous vehicle requestinga second autonomous vehicle view a spatial region that is obstructedfrom a perspective of a sensor of the autonomous vehicle.

FIG. 2 illustrates an exemplary autonomous vehicle.

FIG. 3 illustrates an exemplary driving environment of an autonomousvehicle.

FIG. 4 illustrates another exemplary driving environment of anautonomous vehicle.

FIG. 5 illustrates a further exemplary driving environment of anautonomous vehicle.

FIG. 6 illustrates an exemplary system configured to provide fill datato an autonomous vehicle from an autonomous vehicle fleet.

FIG. 7 is a flow diagram that illustrates an exemplary methodologyexecuted by a computing system of an autonomous vehicle for requesting asecond autonomous vehicle view a spatial region that is obstructed fromthe perspective of a sensor of the autonomous vehicle.

FIG. 8 illustrates an exemplary computing system.

DETAILED DESCRIPTION

Various technologies pertaining to an autonomous vehicle identifying anobstructed region in a field of view of the autonomous vehicle andrequesting a second autonomous vehicle view the obstructed region arenow described with reference to the drawings, wherein like referencenumerals are used to refer to like elements throughout. In the followingdescription, for purposes of explanation, numerous specific details areset forth in order to provide a thorough understanding of one or moreaspects. It may be evident, however, that such aspect(s) may bepracticed without these specific details. In other instances, well-knownstructures and devices are shown in block diagram form in order tofacilitate describing one or more aspects. Further, it is to beunderstood that functionality that is described as being carried out bycertain system components may be performed by multiple components.Similarly, for instance, a component may be configured to performfunctionality that is described as being carried out by multiplecomponents

Moreover, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B. In addition, the articles “a” and“an” as used in this application and the appended claims shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from the context to be directed to a singular form.

Further, as used herein, the terms “component” and “system” are intendedto encompass computer-readable data storage that is configured withcomputer-executable instructions that cause certain functionality to beperformed when executed by a processor. The computer-executableinstructions may include a routine, a function, or the like. It is alsoto be understood that a component or system may be localized on a singledevice or distributed across several devices. Further, as used herein,the term “exemplary” is intended to mean serving as an illustration orexample of something and is not intended to indicate a preference.

Disclosed are various technologies that generally relate to anautonomous vehicle that is configured to identify an obstructed regionbased upon a sensor signal output by a sensor of the autonomous vehicleand is further configured to request a second autonomous vehicle viewthe obstructed region. With more specificity, the autonomous vehicle candefine the obstructed region by identifying a spatial region where theautonomous vehicle lacks visibility, transmit a request for a secondautonomous vehicle to view at least a part of the obstructed region, andreceive data representative of a sensor signal output by a sensor of thesecond autonomous vehicle detecting at least the part of the obscuredregion.

According to various embodiments, a first autonomous vehicle can fill ina map of its surroundings with sensor data (e.g., fill data) generatedby a second autonomous vehicle that is in geographic proximity to thefirst autonomous vehicle. In operation, the first autonomous vehicle canprocess its sensor data and ascertain portions in the 3D environment ofthe first autonomous vehicle where the first autonomous vehicle isunable to acquire data, but such data may be helpful to operation of theautonomous vehicle. Upon recognizing this type of blind spot, the firstautonomous vehicle can report the location of the blind spot to acentral server (e.g., a dispatch server system), which identifies asecond autonomous vehicle in the geographic region. When the secondautonomous vehicle has a view of the blind spot, the second autonomousvehicle causes (in an example) raw sensor data from the secondautonomous vehicle to be transmitted to the first autonomous vehicle.The first autonomous vehicle can then fill in the blind spot with thesensor data generated by the second autonomous vehicle, and the firstautonomous vehicle can maneuver based upon such data. When the secondautonomous vehicle is not in proximity to the first autonomous vehicle,the central server can direct the second autonomous vehicle to alocation where the second autonomous vehicle can capture data that ishelpful to the first autonomous vehicle. In an alternative embodiment,rather than transmitting raw sensor data, the second autonomous vehiclecan perform object detection in a region where the first autonomousvehicle has a blind spot, and can transmit a message to the firstautonomous vehicle as to a type of object (if any) in the blind spot,location of the object in the blind spot, direction of motion of theobject, and other information that may be helpful to the firstautonomous vehicle when maneuvering in the environment of the firstautonomous vehicle. Accordingly, fill data described herein can includeraw sensor data, processed sensor data (e.g., specifying a type of adetected object, location of the detected object, direction of motion ofthe detected object, etc.), or a combination thereof.

Pursuant to an illustration, raw sensor data can be live streamed fromthe second autonomous vehicle to the first autonomous vehicle. Accordingto another illustration, processed sensor data can be live streamed fromthe second autonomous vehicle to the first autonomous vehicle.

With reference now to FIG. 1 , a schematic that illustrates an operatingenvironment 102 of an autonomous vehicle 100 is depicted. The operatingenvironment 102 includes a roadway 104 along which the autonomousvehicle 100 travels. In the illustrated embodiment 102, the roadway 104comprises a first lane 106 and a second lane 108, where traffic in thefirst lane 106 and traffic in the second lane 108 flow in oppositedirections. Further, in the exemplary environment 102, the first lane106 is immediately adjacent the second lane 108.

In the illustrated environment 102, a truck 112 is stopped in the firstlane 106 and the autonomous vehicle 100 traveling in the first lane 106has come behind the truck 112 in the first lane 106. To reach itsintended destination, the autonomous vehicle 100 is to continue alongthe roadway 104 along its current direction of travel. To do so, theautonomous vehicle 100 either waits for the truck 112 to resume movingin the first lane 106 or navigates around the truck 112. To navigatearound the truck 112, the autonomous vehicle 100 exits the first lane106, enters the second lane 108, and travels in the second lane 108against the flow of traffic in the second lane 106 to pass the truck 112until clear to return to the first lane 106. The technologies describedherein can be employed to navigate around the truck 112.

More specifically, the autonomous vehicle 100 includes componentryconfigured to detect when a portion of the roadway along a route of theautonomous vehicle 100 is obscured and to fill in a map with sensor datagenerated by a second autonomous vehicle (or a plurality of otherautonomous vehicles). Componentry of the autonomous vehicle 100 isillustrated in callout 120. The autonomous vehicle 100 includes sensorsystems 122, a transceiver 124, and a computing system 126. The sensorsystems 122 may be configured to output data representative of anenvironment exterior of the autonomous vehicle 100. The sensor systems122 may include one or more sensors that are arranged about theautonomous vehicle 100, as will be described in detail below. Thetransceiver 124 is configured to transmit data from the autonomousvehicle 100 and/or receive data at the autonomous vehicle 100.

The computing system 126 may be in communication with the sensor systems122 and the transceiver 124. The computing system 126 includes aprocessor 128 and memory 130 that includes computer-executableinstructions that are executed by the processor 128. In an example, theprocessor 128 can be or include a graphics processing unit (GPU), aplurality of GPUs, a central processing unit (CPU), a plurality of CPUs,an application-specific integrated circuit (ASIC), a microcontroller, orthe like. The memory 130 includes an obstruction detection system 132and a fill data analysis system 134.

Due to the position of the truck 112 in the first lane 106 relative tothe position of the autonomous vehicle 100, the autonomous vehicle 100lacks visibility with respect to certain portions of the environment102. For instance, as the autonomous vehicle 100 approaches the back ofthe truck 112, a portion of the field of view 110 of the sensor systems122 is obstructed by the truck 112 (e.g., the sensor systems 122 areunable to “see” around the truck 112). Thus, there is an obstructedregion 114 from the perspective of the autonomous vehicle 100. Theobstructed region 114 is a spatial region where the autonomous vehicle100 has little or no visibility—e.g., the autonomous vehicle 100 isunable to determine whether there is an object (e.g., a vehicle,pedestrian, bicyclist, static object) in the obstructed region 114,whether there is an object moving in the obstructed region 114, thecondition of the roadway in obstructed region 114, etc. The obstructedregion 114 includes a portion of the second lane 108 of the roadway 104,thus it is possible that a vehicle is traveling in the second lane 108but the vehicle may not be detected by the autonomous vehicle 100 due tothe vehicle being in the obstructed region 114. As will be describedbelow, the computing system 126 is configured to detect when an object(the truck 112) has obscured the field of view of the sensor systems 122and to fill in a map of the operating environment 102 with sensor datagenerated by the second autonomous vehicle 116. The map can be filled byrequesting other autonomous vehicle(s) provide sensor data correspondingto at least a portion of the obscured region (or information derivedfrom such sensor data). The request may be sent directly to anotherautonomous vehicle or to a server (e.g., a dispatch server system),which may further send the request to the other autonomous vehicle.

The obstruction detection system 132 is configured to detect when aportion of a field of view 110 of the sensor systems 122 is obstructedby an object (e.g., the truck 112). Responsive to detecting that aportion of the field of view 110 of the sensor systems 122 isobstructed, the computing system 126 transmits, via the transceiver 124,a request for a second autonomous vehicle to view at least a part of theobscured portion.

In one embodiment, the request is directly transmitted from theautonomous vehicle 100 to a second autonomous vehicle 116. In anotherembodiment, the request is transmitted from the autonomous vehicle 100to a dispatch server system, which then transmits a correspondingrequest to a second autonomous vehicle 116, as will be described indetail below. In an embodiment, the dispatch server system may befurther configured to establish a peer-to-peer connection networkbetween the autonomous vehicle 100 and the second autonomous vehicle116. In another embodiment, the dispatch server system provides to theautonomous vehicle 100 suitable identification information about thesecond autonomous vehicle 116 that is used by the autonomous vehicle 100to establish a peer-to-peer connection network between the autonomousvehicle 100 and the second autonomous vehicle 116. The fill data,described below, may be transmitted over this peer-to-peer connectionnetwork. In other embodiments, communication between the autonomousvehicle 100 and the second autonomous vehicle can pass through thedispatch server system.

The computing system 126 may be configured to transmit supplementaryinformation in addition to the request described above. For example, thecomputing system 126 can transmit geolocation data of the autonomousvehicle 100 and/or geolocation data of an object (e.g., the truck 112 inthe example of FIG. 1 ) when the obstruction detection system 132detects the portion of a field of view 110 of the sensor systems 122 isobstructed by the object. In another example, the computing system 126can transmit data specifying a direction the autonomous vehicle 100 istraveling relative to the object. In a further example, the computingsystem 126 can transmit orientation data representative of anorientation of the autonomous vehicle 100 relative to the object. In yetanother example, the computing system 126 may transmit a timestamp ofwhen the obstruction detection system 132 detects the portion of a fieldof view 110 of the sensor systems 122 is obstructed by the object. Anysuitable information may be transmitted.

The obstruction detection system 132 may be further configured toselectively transmit the request based on the portion of the field ofview 110 obstructed. Selectively transmitting the request and/or theinformation can be used to regulate the allocation of processing powerof the autonomous vehicle 100. For instance, the request and/orinformation is transmitted where the portion of the field of view 110obstructed by the object includes the roadway 104 the autonomous vehicle100 is traveling along. Whereas, when the portion of the field of view110 obstructed by the object does not include the roadway 104, norequest may be transmitted.

The fill data analysis system 134 is configured to receive, via thetransceiver 124, and analyze data representative of a field of view 118of sensor systems in the second autonomous vehicle 116 detecting atleast a part of the obscured portion (e.g., the obscured region 114).The fill data analysis system 134 may then use this data from the secondautonomous vehicle 116 to fill in portions of a map of the operatingenvironment 102 that are obscured by the object (as well as any otherobject). For instance, in the illustrated embodiment, the fill dataanalysis system 134 may use data from the second autonomous vehicle 116to determine that there is no vehicle in the obstructed region 114 ofthe second lane 108. The autonomous vehicle 100 can then use thisdetermination to navigate around the truck 112.

Similar to the request described above, in one embodiment, the fill datais directly transmitted from the second autonomous vehicle 116 to theautonomous vehicle 100. In another embodiment, the fill data istransmitted from the second autonomous vehicle 116 to a dispatch serversystem which then transmits it to the autonomous vehicle 100.

Turning now to FIG. 2 , illustrated is a block diagram of the exemplaryautonomous vehicle 100 of FIG. 1 . The autonomous vehicle 100 cannavigate about roadways without human conduction based upon sensorsignals output by the sensor systems 122 of the autonomous vehicle 100.The sensor systems 122 of the autonomous vehicle 100 illustrated in FIG.1 include a plurality of sensor systems in FIG. 2 , namely, a sensorsystem 1 200, . . . , and a sensor system N 202 (collectively referredto herein as sensor systems 200-202). The sensor systems 200-202 may beof different types and may be arranged about the autonomous vehicle 100.For example, the sensor system 1 200 may be a lidar sensor system andthe sensor system N 202 may be a camera (image) system. Other exemplarysensor systems 200-202 included are radar sensor systems, globalpositioning system (GPS) sensor systems, sonar sensor systems, infraredsensor systems, and the like.

The autonomous vehicle 100 further includes several mechanical systemsthat are used to effectuate appropriate motion of the autonomous vehicle100. For instance, the mechanical systems can include, but are notlimited to, a vehicle propulsion system 204, a braking system 206, and asteering system 208. The vehicle propulsion system 204 may be anelectric motor, an internal combustion engine, a combination thereof,and/or the like. The braking system 206 can include an engine brake,brake pads, actuators, and/or any other suitable componentry that isconfigured to assist in decelerating the autonomous vehicle 100. Thesteering system 208 includes suitable componentry that is configured tocontrol the direction of the movement of the autonomous vehicle 100.

The autonomous vehicle 100 additionally comprises the computing system126 that is in communication with the sensor systems 200-202, thevehicle propulsion system 204, the braking system 206, the steeringsystem 208, and/or the transceiver 124. Again, the computing system 122includes the processor 128 and the memory 130.

In addition to including the obstruction detection system 132 and thefill data analysis system 134 as described above, the memory 130includes a control system 214 configured to control operation of thevehicle propulsion system 204, the braking system 206, and/or thesteering system 208. Additionally, or alternatively, the memory 126 canfurther include a classification system 210 that is configured toclassify an object observed by the autonomous vehicle 100, as will bedescribed in detail below.

The computing system 122 may further include a data store 216. The datastore 216 includes classification data 218 and/or map data 220. Theclassification data 218 can include data indicative of one or morepredefined object classes. Each object class can be further broken downinto subordinate classes. For instance, an object class of vehicle canbe broken down into car, bus, delivery vehicle, mail truck, motorcycle,and/or the like. The autonomous vehicle 100 can use this data toclassify objects in the environment exterior of the autonomous vehicle100. For instance, the classification system 210 can be configured touse the classification data 218 to classify objects in the environmentexterior of the autonomous vehicle 100 that are obscuring a portion ofthe field of view of the sensor systems 200-202. The classificationsystem 210 may be configured to receive output from the sensor systems200-202 representative of an environment exterior of the autonomousvehicle 100 detected by the sensor systems 200-202. The classificationsystem 210 can then access the classification data 218 to classify oneor more objects in the environment exterior of the autonomous vehicle100 that are obscuring a portion of the field of view of the sensorsystems 200-202. Similar to the supplementary information describedabove with reference to FIG. 1 , the object classification may betransmitted, via the transceiver 124, in addition to the request.

The map data 220 can include data indicative of operating environmentstraversed by one or more autonomous vehicles. The map data 220 caninclude any suitable information that can be employed to navigate anautonomous vehicle 100 in the operating environment 102. For instance,the map data 220 can include locations of lane boundaries, locations oftraffic signs and signals, locations of manholes, speed limits forroadways, etc.

The map data 220 may yet further include one or more locations ofdetected objects (e.g., the truck 112) that obscured a portion of thefield of view of the sensor systems 200-202 of one or more autonomousvehicles. Autonomous vehicle(s) may be routed to avoid these locations.The locations may also be confirmed and/or updated based on anautonomous vehicle(s) passing the location and detecting the presence,absence, and/or new location of the object. For example, a firstautonomous vehicle may detect a location of a parked truck that blocks aportion of a field of view of the first autonomous vehicle. The map data220 may be updated to include the location of the obscuring object (theparked truck). A second autonomous vehicle may pass by the notedlocation of the parked truck and detect that the truck is no longer atthe noted location. The map data 220 may then be updated to remove theobscuring object indicator at that location.

Further, the object classification data may be used to update and/oralter detected locations of obscuring objects. For example, locations ofcertain object classes may be timed out, such that indicators of thelocation of the map data 220 may be removed after a threshold period oftime unless a second autonomous vehicle confirms the location of theobject. For instance, classes of objects that are likely to move from adetected location subsequent to detection may be timed out. One examplewould be a bus that is likely to move from a detected obscuring locationsubsequent to detection.

By updating and/or altering indicators of locations of obscuringobjects, the map data 220 will not be covered with old or outdatedlocation indicators. Further, routing of autonomous vehicle(s) will beimproved because the autonomous vehicle(s) will not be needlessly routedaround no longer appropriate location indicators.

Turning now to FIG. 3 , a route taken by the second autonomous vehiclemay be selected and/or altered based on the request from the autonomousvehicle 100. For instance, a second autonomous vehicle may be routedwithin a threshold distance of the obscured portion to view at least apart of the obstructed region. In another example, a current route of asecond autonomous vehicle may be altered such that the altered routetakes the second autonomous vehicle within a threshold distance of theobscured portion to view at least a part of the obscured portion. Anysuitable threshold for the distance may be selected and may varydepending on the sensor systems in the second autonomous vehicle, theclassification of the obscuring object, the exterior environment nearthe obscured portion (e.g., building(s), open land), and/or an amount ofthe portion of the field of view obscured.

In an embodiment illustrated in FIG. 3 , an object, car 300, obscures aportion of a field of view 110 of an autonomous vehicle 100. Subsequentto detecting that the portion is obscured, the autonomous vehicle 100transmits a request for a second autonomous vehicle 304 to view at leasta part of the obscured portion 302 within a field of view 306 of thesecond autonomous vehicle 304. The second autonomous vehicle 304 iscurrently being routed from the illustrated initial position to adestination 308. The second autonomous vehicle 304 may be routed to thedestination 308 such that the second autonomous vehicle 304 passeswithin a threshold distance of the obscured portion 302. In theillustrated embodiment, the second autonomous vehicle 304 may take afirst route 310, a second route 312, or a third route 314 to reach thedestination 308. However, only the first route 310 and the second route312 pass within a threshold distance of the obscured portion 302. Thus,if the second autonomous vehicle 304 takes the third route 314 to thedestination 308 it does not pass within a threshold distance of theobscured portion 302.

Any suitable factor or factors may be used to select between the firstroute 310 and the second route 312. For instance, anticipated traveltime of the different routes 310 and 312 may be used to selecttherebetween. In another example, an amount of the obscured portion 302viewed by the second autonomous vehicle 304 along the different routes310 and 312 may be used to select therebetween.

Altering a route of an already traveling second autonomous vehicle basedon a request of a first autonomous vehicle may be selectively applied.For instance, a route of a second autonomous vehicle may be altered whenan anticipated travel time along the altered route is within a thresholddifference from the anticipated travel time of the current route. Bycontrolling when a current route of a second autonomous vehicle isaltered, travel time of the second autonomous vehicle is favored whilealso having the second autonomous vehicle view the obstructed region.

In an embodiment illustrated in FIG. 3 , a second autonomous vehicle 304is traveling along route 314 to its destination 308, when an autonomousvehicle 100 requests a view of an obscured portion 302 of a field ofview 110 of the autonomous vehicle 100. Responsive to the request, thecurrent route of the second autonomous vehicle 304 may be altered (e.g.,by the dispatch server system, the second autonomous vehicle 304, etc.)such that the second autonomous vehicle 304 travels within a thresholddistance from the obscured portion 302 to view at least a portion of theobscured portion 302. In the illustrated embodiment, two potentialaltered routes 310 and 312 for the second autonomous vehicle 304 areillustrated. In order to choose between the two potential routes,anticipated travel times for each altered route 310 and 312 can bedetermined and then each anticipated travel time can be compared to ananticipated travel time for the current route 314 to determine whethereither falls within the threshold difference. For example, ananticipated travel time along a first altered route 310 may fall outsidethe threshold while an anticipated travel time along a second alteredroute 312 falls within the threshold. Accordingly, the second autonomousvehicle 304 will travel along the second altered route 312 to view apart of the obstructed region 302. According to another example, it iscontemplated that a duration of time that the autonomous vehicle 100 hasbeen stopped behind the car 300 can be utilized when selecting a route(e.g., a longer route may be selected for the second autonomous vehicle304 if the autonomous vehicle 100 has been waiting a longer time behindthe car 300).

Turning now to FIG. 4 , illustrated is an embodiment in which a fleet ofautonomous vehicles are operating within the driving environment of theautonomous vehicle 100. In order to regulate how much bandwidth is to beallocated for transmitting data from and receiving data at theautonomous vehicle 100, only a select autonomous vehicle(s) from thefleet of autonomous vehicles will provide the fill data and/or receivethe request for fill data. Any suitable criteria may be used forselecting which autonomous vehicle from the fleet of autonomous vehiclesprovides the fill data and/or receives the request for fill data. Forexample, the autonomous vehicle may be selected based on proximity tothe autonomous vehicle 100. In another example, the autonomous vehiclemay be selected based on the current route of the autonomous vehicle. Inyet another example, the autonomous vehicle may be selected based on adirection at which the autonomous vehicle approaches the obstructingobject.

In the illustrated embodiment, a fleet of autonomous vehicles, includinga first autonomous vehicle 404, a second autonomous vehicle 406, a thirdautonomous vehicle 408, and a fourth autonomous vehicle 410(collectively referred to as “autonomous vehicles 404-410”), areoperating in a driving environment of the autonomous vehicle 100. Eachof the autonomous vehicles 404-410 has a respective field of view412-418 and each are traveling along a respective route 420-426.

The autonomous vehicle 100 has detected that an object (truck 400) hasobscured a portion 402 of the field of view 110 of the autonomousvehicle 100. Responsive to detecting the obscured portion 402, theautonomous vehicle 100 transmits a request for one or more of autonomousvehicle(s) from the fleet of autonomous vehicles 404-410 to view theobscured portion 402.

The autonomous vehicle(s), if any, may be selected from the fleet ofautonomous vehicles 404-410 via any suitable method. For instance, aprocess of elimination may be used to filter down the number ofautonomous vehicles considered. For example, autonomous vehiclestraveling along routes away from the obstructed region 402 may beeliminated from consideration. This is illustrated by the thirdautonomous vehicle 408 that is traveling along route 424 away from theobstructed region 402. In another example, autonomous vehicles travelingalong a route in the same lane and direction as the autonomous vehicle100 may be eliminated from consideration because the autonomous vehicleswould not be able to “see” around the object (the truck 112). This isillustrated by the fourth autonomous vehicle 410 which is travelingalong route 426 which is the same direction as the autonomous vehicle100. In yet another example, where an autonomous vehicle(s) is currentlytraveling along a route that passes through the obstructed region 402,autonomous vehicles(s) that are currently traveling along a route thatdoes not pass through the obstructed region 402 may be eliminated fromconsideration. Therefore, because the first autonomous vehicle 404 isalready traveling along route 420 that passes through the obstructedregion 402, the second autonomous vehicle 406 traveling along route 422that doesn't pass through the obstructed region 402 may be eliminatedfrom consideration. Accordingly, by process of elimination, theautonomous vehicle 100 need only receive fill data from the firstautonomous vehicle 404.

In the preceding embodiments, a single autonomous vehicle was used toprovide the fill data to the autonomous vehicle 100. However, it iscontemplated that more than one autonomous vehicle can be used toprovide the fill data. For instance, where the obscured portion includesintersecting roads, it may be desirable for autonomous vehicles toprovide fill data from each of the intersecting roads.

Illustrated in FIG. 5 is a driving environment comprising a firstroadway 500 that intersects with a second roadway 502. The first roadway500 includes a first lane 504 (where the autonomous vehicle 100 istraveling in the first lane 504) and a second lane 506, where trafficflows in opposite directions in the first lane 504 and the second land506. The second roadway 502 includes a first lane 508 and a second lane510, where traffic flows in opposite directions in the first lane 508and the second lane 510. The intersection of the first roadway 500 andthe second roadway 502 further includes a traffic light 518 that maycontrol the flow of traffic through the intersection.

As the autonomous vehicle 100 travels in the first lane 504, a truck 512is stopped in the first lane 504 and the autonomous vehicle 100traveling in the first lane 504 has come behind the stopped truck 512 inthe first lane 504. As the autonomous vehicle 100 approaches the back ofthe truck 512, a portion of the field of view 110 of the sensor systemsin the autonomous vehicle 100 is obstructed by the truck 512, theresulting obstructed region illustrated at 514. In the illustratedembodiment, the obstructed region 514 includes a portion of the secondlane 506, a portion of the first lane 504 between a front of the truck512 and the intersection, and the intersection itself. In order tonavigate around the truck 512, the autonomous vehicle 100 determinesthat there are no objects in the portion of the second lane 506 in theobscured region 514 and that there is room to reenter the first lane 504between the front of the truck 512 and the intersection.

To this end, the autonomous vehicle 100 may request a second autonomousvehicle view a portion of the obstructed region 514 and provide filldata to the autonomous vehicle 100. A second autonomous vehicle 524currently on an opposing side of the intersection and travelling in thesecond lane 506 may view a part of the obstructed region 514 that is inthe field of view 526 of the second autonomous vehicle 524. The secondautonomous vehicle 524 may determine that currently there are no objectsin the portion of the second lane 506 in the obstructed region 514and/or that there is an object (car 516) in the portion of the firstlane 504 between a front of the truck 512 and the intersection. Thesecond autonomous vehicle 524 may further determine that there is roombetween the front of the truck 512 and the car 516 for the autonomousvehicle 100 to reenter the first lane 504 after traveling around thetruck 512.

However, because of the presence of the intersection, between the secondautonomous vehicle 524 and the portion of the second lane 506 theautonomous vehicle 100 would be traveling through to maneuver around thetruck 512, the second autonomous vehicle 524 may not be able todetermine that there would be no objects in the portion of the secondlane 506 as the autonomous vehicle 100 travels in the second lane 506around the truck 512. More particularly, an object may enter the secondlane 506 from the second roadway 502 while the autonomous vehicle 100 istravelling in the second lane 506 around the truck 512. For instance,the second autonomous vehicle 524 may not be able to detect whether thesecond roadway 502 has a green light at the traffic light 518 permittingan object in the second roadway 502 to enter the second lane 506, whilethe autonomous vehicle 100 is travelling in the second lane 506 aroundthe truck 512. Accordingly, a third autonomous vehicle 520 with a fieldof view 522 traveling in a first lane 508 of the second roadway 502toward the intersection may present second fill data detecting whetherthe second roadway 502 has a green light at the traffic light 518 and/orwhether the second roadway 502 includes any objects that can enter thesecond lane 506 of the first roadway 500 while the autonomous vehicle100 is travelling in the second lane 506 around the truck 512.

The autonomous vehicle 100 can then receive this fill data from thesecond autonomous vehicle 524 and the third autonomous vehicle 520 andthen use this fill data to fill in portions of the obstructed region514.

Turning now to FIG. 6 , illustrated is a system 600 configured toprovide an autonomous vehicle with fill data from one or more autonomousvehicle in an autonomous vehicle fleet. In one embodiment, illustratedin FIG. 6 , the system 600 includes a computing system (e.g., dispatchserver system 602) that is in communication with an autonomous vehicle(e.g., autonomous vehicle 100). In order to transmit data to thedispatch server system 602 and/or receive data from the dispatch serversystem 602, the autonomous vehicle 100 includes a transceiver(transceiver 124). The transceiver is configured to transmit data fromthe autonomous vehicle 100 and/or receive data at autonomous vehicle100. Thus, the autonomous vehicle 100 can be in communication with thedispatch server system 602.

In the illustrated embodiment, the dispatch server system 602 is incommunication with a plurality of other autonomous vehicles, namely,autonomous vehicle 1 604, . . . , and autonomous vehicle X 606, where Xcan be substantially any integer greater than or equal to 2(collectively referred to herein as autonomous vehicles 604-606). Theautonomous vehicles 604-606 may be in the same fleet of autonomousvehicles and/or may be part of different fleets of autonomous vehicles.In another embodiment, the dispatch server system 602 can be incommunication with a single autonomous vehicle (e.g., the autonomousvehicle 1 604).

In order to transmit data to the autonomous vehicle 100 and/or theautonomous vehicles 604-606 and/or receive data therefrom, the dispatchserver system 602 may further include a transceiver 616. The transceiver616 is configured to transmit data from the dispatch server system 602and/or receive data at the dispatch server system 602. Thus, thedispatch server system 602 can be in communication with the autonomousvehicle 100 and/or the autonomous vehicles 604-606.

The dispatch server system 602 is configured to receive a request fromthe autonomous vehicle 100 for a second autonomous vehicle to view atleast a part of the obscured portion of the field of view, as describedabove, and, in response to receiving the request, transmit a signal toone or more of the autonomous vehicles 604-606. The signal transmittedto the autonomous vehicle(s) can indicate a request for the autonomousvehicle(s) to view the obscured portion of the field of view of theautonomous vehicle 100 while the autonomous vehicle(s) travels along aroute the autonomous vehicle is following in an environment.

In one embodiment, the dispatch server system 602 may transmit differentsignals based on the travel paths of the autonomous vehicles 604-606 inan environment. For instance, the dispatch server system 602 cantransmit the signal when a route taken by one or more of the autonomousvehicles 604-606 passes within a threshold distance of the obscuredportion. In another embodiment, the dispatch server system 602 maytransmit the same signal to the autonomous vehicles 604-606, regardlessof the travel path of the autonomous vehicles 604-606. In anotherembodiment, the dispatch server system 602 may be configured toestablish a communication network between the autonomous vehicle 100 andone or more of the autonomous vehicles 604-606.

According to an example, the signal transmitted by the dispatch serversystem 602 can specify a route to be taken by a particular one of theautonomous vehicles 604-606. Pursuant to another example, the signaltransmitted by the dispatch server system 602 can specify a location bywhich one or more of the autonomous vehicles 604-606 is to pass.

In order to achieve the actions described above, the dispatch serversystem 602 can include a processor 608 and memory 610 that includescomputer-executable instructions that are executed by the processor 608.The memory 610 may include an autonomous vehicle search system 612, afill data analysis system 614, and/or a communication network system616.

The autonomous vehicle search system 612 is configured to transmit asignal to one or more of the autonomous vehicles 604-606. In oneembodiment, the autonomous vehicle search system 612 may be configuredto transmit a similar signal to one or more of the autonomous vehicles604-606. In another embodiment, the autonomous vehicle search system 612may be configured to transmit the signal based on a travel path of theautonomous vehicle. More specifically, the autonomous vehicle searchsystem 612 may transmit the signal only when a travel path of theautonomous vehicle takes the autonomous vehicle within a thresholddistance of the obscured portion. In another embodiment, the autonomousvehicle search system 612 may select which autonomous vehicle totransmit the signal too based on the process of elimination describedabove with respect to FIG. 4 .

The fill data analysis system 614 is configured to receive fill datafrom one or more of the autonomous vehicles 604-606. In one embodiment,the fill data analysis system 614 may function similar to a conduit byreceiving, via the transceiver 616, fill data from one or more of theautonomous vehicles 604-606 and transmitting fill data, via thetransceiver 616, to the autonomous vehicle 100. In one example, the filldata analysis system 614 may transmit fill data to the autonomousvehicle 100 that is similar to the fill data received from one or moreof the autonomous vehicles 604-606. In another example, the fill dataanalysis system 614 may modify or process fill data received from one ormore of the autonomous vehicles 604-606 before transmitting fill data tothe autonomous vehicle 100. Fill data transmitted to the autonomousvehicle 100 may be configured for use by the autonomous vehicle 100 tofill in a part of the obscured portion.

In another embodiment, the fill data analysis system 614 may receivedata indicative of the obstructed portion of the field of view of theautonomous vehicle 100 and generate data of a modified field of view byfilling in a part of the obstructed portion with fill data received fromone or more of the autonomous vehicles 604-606. This data of themodified field of view may be transmitted to the autonomous vehicle 100for use by the autonomous vehicle 100.

The communication network system 616 is configured to enablecommunication between the autonomous vehicle 100 and one or more of theautonomous vehicles 604-606. In another embodiment, the communicationnetwork system 616 establishes the peer-to-peer network connectionbetween the autonomous vehicle 100 and the one or more autonomousvehicles 604-606. Any suitable method may be employed for establishingthis peer-to-peer network connection.

FIG. 7 illustrates an exemplary methodology 700 for sharing sensor datato assist in maneuvering an autonomous vehicle. While the methodology isshown as being a series of acts that are performed in a sequence, it isto be understood and appreciated that the methodology is not limited bythe order of the sequence. For example, some acts can occur in adifferent order than what is described herein. In addition, an act canoccur concurrently with another act. Further, in some instances, not allacts may be required to implement a methodology described herein.

Moreover, the acts described herein may be computer-executableinstructions that can be implemented by one or more processors and/orstored on a computer-readable medium or media. The computer-executableinstructions can include a routine, a sub-routine, programs, a thread ofexecution, and/or the like. Still further, results of acts of themethodologies can be stored in a computer-readable medium displayed on adisplay device, and/or the like.

Referring now to FIG. 7 an exemplary methodology 700 for sharing sensordata to assist in maneuvering an autonomous vehicle is illustrated. Themethodology 700 begins at 702, and at 704, a computing system detectsthat a portion of a roadway along a route of the autonomous vehicle in afield of a view of a sensor system of the autonomous vehicle isobscured. At 606, responsive to detecting the portion of the roadway isobscured, the computing system can transmit a request for a secondautonomous vehicle to view the portion of the roadway. A position of thesecond autonomous vehicle relative to the portion of the roadway isdifferent from a position of the autonomous vehicle relative to theportion of the roadway when detecting the portion of the roadway isobscured. At 708, responsive to transmitting the request, the computingsystem can further receive fill data representative of an output of asecond sensor system of the second autonomous vehicle detecting theportion of the roadway. The methodology 700 concludes at 710.

In an embodiment of the methodology 700, the step of transmitting therequest comprises transmitting the request to a dispatch server system.The request may be configured to cause the dispatch server system toselect the second autonomous vehicle from a fleet of autonomousvehicles. In one version of this embodiment, the second autonomousvehicle is selected based on a route of the second autonomous vehiclepassing within a threshold distance from the portion of the roadway. Inanother version of this embodiment, responsive to detecting the portionof the roadway is obscured, transmitting direction data to the dispatchserver system. The direction data is representative of a direction oftravel of the autonomous vehicle relative to the portion of the roadwaywhen detecting the portion of the roadway is obscured. The secondautonomous vehicle may be selected based direction of travel of thesecond autonomous vehicle when viewing the portion of the roadway. Thedirection of travel of the second autonomous vehicle is different fromthe direction of travel of the autonomous vehicle.

In another embodiment of the methodology 700, the computing system canestablish a peer-to-peer network connection between the autonomousvehicle and the second autonomous vehicle prior to receiving the filldata. The fill data can be received by the autonomous vehicle over thepeer-to-peer network connection. In a further embodiment of themethodology 700, the computing system can be an intermediary between theautonomous vehicle and the second autonomous vehicle.

In a further embodiment of the methodology 700, responsive to detectingthe portion of the roadway is obscured, a direction of travel of theautonomous vehicle relative to the portion of the roadway when detectingthe portion of the roadway is obscured, a timestamp of when the obscuredportion of the roadway was detected, and/or a route of the autonomousvehicle can be transmitted.

In yet another embodiment of the methodology 700, responsive todetecting the portion of the roadway is obscured, a determination can bemade concerning whether an object is obscuring the portion of theroadway. Moreover, an object class of the object can be identified.Examples of object classes can include a vehicle, building, sign, orrubble; however, other object classes can additionally or alternativelybe utilized. Further, data representative of the object class can betransmitted to a dispatch server system.

Referring now to FIG. 8 , a high-level illustration of an exemplarycomputing device that can be used in accordance with the systems andmethodologies disclosed herein is illustrated. For instance, thecomputing device 800 may be or include the computing system 126.Pursuant to another example, the computing system 126 can include thecomputing device 800. According to another example, the computing device800 may be or include the dispatch server system 602. In yet anotherexample, the dispatch server system 602 can include the computing device800. The computing device 800 includes at least one processor 802 thatexecutes instructions that are stored in a memory 804. The instructionsmay be, for instance, instructions for implementing functionalitydescribed as being carried out by one or more components discussed aboveor instructions for implementing one or more methods described above.The processor 802 may be a GPU, a plurality of GPUs, a CPU, a pluralityof CPUs, a multi-core processor, etc. The processor 802 may access thememory 804 by way of a system bus 806. In addition to storing executableinstructions, the memory 804 may also store information pertaining toobscured regions, fill data from other autonomous vehicle(s), etc.

The computing device 800 additionally includes a data store 810 that isaccessible by the processor 802 by way of the system bus 806. The datastore 810 may include executable instructions, information pertaining toobscured regions, fill data from other autonomous vehicle(s), etc. Thecomputing device 800 also includes an input interface 808 that allowsexternal devices to communicate with the computing device 800. Forinstance, the input interface 808 may be used to receive instructionsfrom an external computer device, from a user, etc. The computing device800 also includes an output interface 812 that interfaces the computingdevice 800 with one or more external devices. For example, the computingdevice 800 may display text, images, etc. by way of the output interface812.

Additionally, while illustrated as a single system, it is to beunderstood that the computing device 800 may be a distributed system.Thus, for instance, several devices may be in communication by way of anetwork connection and may collectively perform tasks described as beingperformed by the computing device 800.

Various functions described herein can be implemented in hardware,software, or any combination thereof. If implemented in software, thefunctions can be stored on or transmitted over as one or moreinstructions or code on a computer-readable medium. Computer-readablemedia includes computer-readable storage media. A computer-readablestorage media can be any available storage media that can be accessed bya computer. By way of example, and not limitation, suchcomputer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM orother optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium that can be used to store desiredprogram code in the form of instructions or data structures and that canbe accessed by a computer. Disk and disc, as used herein, includecompact disc (CD), laser disc, optical disc, digital versatile disc(DVD), floppy disk, and blu-ray disc (BD), where disks usually reproducedata magnetically and discs usually reproduce data optically withlasers. Further, a propagated signal is not included within the scope ofcomputer-readable storage media. Computer-readable media also includescommunication media including any medium that facilitates transfer of acomputer program from one place to another. A connection, for instance,can be a communication medium. For example, if the software istransmitted from a website, server, or other remote source using acoaxial cable, fiber optic cable, twisted pair, digital subscriber line(DSL), or wireless technologies such as infrared, radio, and microwave,then the coaxial cable, fiber optic cable, twisted pair, DSL, orwireless technologies such as infrared, radio, and microwave areincluded in the definition of communication medium. Combinations of theabove should also be included within the scope of computer-readablemedia.

Alternatively, or in addition, the functionally described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (ASICs),Application-specific Standard Products (ASSPs), System-on-a-chip systems(SOCs), Complex Programmable Logic Devices (CPLDs), etc.

As described herein, one aspect of the present technology is thegathering and use of data available from various sources to improvequality and experience. The present disclosure contemplates that in someinstances, this gathered data may include personal information. Thepresent disclosure contemplates that the entities involved with suchpersonal information respect and value privacy policies and practices.

What has been described above includes examples of one or moreembodiments. It is, of course, not possible to describe everyconceivable modification and alteration of the above devices ormethodologies for purposes of describing the aforementioned aspects, butone of ordinary skill in the art can recognize that many furthermodifications and permutations of various aspects are possible.Accordingly, the described aspects are intended to embrace all suchalterations, modifications, and variations that fall within the spiritand scope of the appended claims. Furthermore, to the extent that theterm “includes” is used in either the details description or the claims,such term is intended to be inclusive in a manner similar to the term“comprising” as “comprising” is interpreted when employed as atransitional word in a claim.

What is claimed is:
 1. A server system, comprising: a processor; andmemory that stores computer-executable instructions that, when executedby the processor, cause the processor to perform acts comprising:receiving, from a first autonomous vehicle, a request for an autonomousvehicle in a fleet of autonomous vehicles to view a portion of a roadwayalong a route of the first autonomous vehicle detected as being obscuredin a field of view of a first sensor system of the first autonomousvehicle; selecting a second autonomous vehicle from the fleet ofautonomous vehicles, wherein a position of the second autonomous vehiclerelative to the portion of the roadway when viewing the portion of theroadway is different from a position of the first autonomous vehiclerelative to the portion of the roadway when detecting the portion of theroadway is obscured; and causing the second autonomous vehicle totransmit fill data for the first autonomous vehicle, the fill data beingrepresentative of an output of a second sensor system of the secondautonomous vehicle detecting the portion of the roadway in a field ofview of the second sensor system of the second autonomous vehicle. 2.The server system of claim 1, the acts further comprising: receiving thefill data from the second autonomous vehicle; and transmitting the filldata to the first autonomous vehicle.
 3. The server system of claim 1,wherein the fill data is transmitted from the second autonomous vehicleto the first autonomous vehicle over a peer-to-peer connection network.4. The server system of claim 1, wherein the second autonomous vehicleis selected based on a current route of the second autonomous vehiclepassing within a threshold distance from the portion of the roadway. 5.The server system of claim 1, wherein the second autonomous vehicle isselected based on a direction of travel of the second autonomous vehiclewhen viewing the portion of the roadway, wherein the direction of travelof the second autonomous vehicle is different from a direction of travelof the first autonomous vehicle relative to the portion of the roadwaywhen detecting the portion of the roadway is obscured.
 6. The serversystem of claim 1, wherein the second autonomous vehicle is selectedbased on a proximity of the second autonomous vehicle to the portion ofthe roadway.
 7. The server system of claim 1, the acts furthercomprising: controlling a route of the second autonomous vehicle suchthat the portion of the roadway is viewable by the second sensor systemof the second autonomous vehicle along the route.
 8. The server systemof claim 7, wherein the route of the second autonomous vehicle isfurther controlled based on an anticipated travel time of the secondautonomous vehicle along the route.
 9. The server system of claim 7,wherein the route of the second autonomous vehicle is further controlledbased on an amount of the portion of the roadway viewable by the secondsensor system of the second autonomous vehicle.
 10. An autonomousvehicle, comprising: a sensor system; and a computing system that is incommunication with the sensor system, wherein the computing systemcomprises: a processor; and memory that stores computer-executableinstructions that, when executed by the processor, cause the processorto perform acts comprising: receiving, from a server system, a requestfor fill data outputted by the sensor system, the request being for thefill data representative of a portion of a roadway in a field of view ofthe sensor system where the portion of the roadway is obscured in afield of view of a differing sensor system of a differing autonomousvehicle; capturing the fill data representative of the portion of theroadway outputted by the sensor system; and transmitting the fill datarepresentative of the portion of the roadway to the differing autonomousvehicle.
 11. The autonomous vehicle of claim 10, the acts furthercomprising: receiving, from the server system, information specifying aroute for the autonomous vehicle such that the portion of the roadway isviewable by the sensor system along the route; and controlling theautonomous vehicle to travel along the route.
 12. The autonomous vehicleof claim 10, wherein the fill data representative of the portion of theroadway is transmitted to the differing autonomous vehicle via theserver system.
 13. The autonomous vehicle of claim 10, wherein the filldata representative of the portion of the roadway is transmitted to thediffering autonomous vehicle over a peer-to-peer connection network. 14.The autonomous vehicle of claim 10, wherein the fill data comprises rawsensor data streamed from the autonomous vehicle to the differingautonomous vehicle.
 15. The autonomous vehicle of claim 10, wherein thefill data comprises processed sensor data streamed from the autonomousvehicle to the differing autonomous vehicle.
 16. The autonomous vehicleof claim 15, wherein the processed sensor data comprises at least one ofdata specifying a type of a detected object, data specifying a locationof the detected object, or data specifying a direction of motion of thedetected object.
 17. A method executed by a server system, the methodcomprising: receiving, from a first autonomous vehicle, a request for anautonomous vehicle in a fleet of autonomous vehicles to view a portionof a roadway along a route of the first autonomous vehicle detected asbeing obscured in a field of view of a first sensor system of the firstautonomous vehicle; selecting a second autonomous vehicle from the fleetof autonomous vehicles, wherein a position of the second autonomousvehicle relative to the portion of the roadway when viewing the portionof the roadway is different from a position of the first autonomousvehicle relative to the portion of the roadway when detecting theportion of the roadway is obscured; and causing the second autonomousvehicle to transmit fill data for the first autonomous vehicle, the filldata being representative of an output of a second sensor system of thesecond autonomous vehicle detecting the portion of the roadway in afield of view of the second sensor system of the second autonomousvehicle.
 18. The method of claim 17, further comprising: controlling aroute of the second autonomous vehicle such that the portion of theroadway is viewable by the second sensor system of the second autonomousvehicle along the route.
 19. The method of claim 17, wherein the secondautonomous vehicle is selected based on at least one of a current routeof the second autonomous vehicle, a direction of travel of the secondautonomous vehicle, or a current location of the second autonomousvehicle.
 20. The method of claim 17, wherein the fill data istransmitted from the second autonomous vehicle to the first autonomousvehicle over a peer-to-peer connection network.